import argparse
from collections import OrderedDict
from pathlib import Path

import torch

from transformers import (
    VisualBertConfig,
    VisualBertForMultipleChoice,
    VisualBertForPreTraining,
    VisualBertForQuestionAnswering,
    VisualBertForVisualReasoning,
)
from transformers.utils import logging


logging.set_verbosity_info()
lowerCamelCase__  =					logging.get_logger(__name__)

lowerCamelCase__  =					[
    ('''bert.bert''', '''visual_bert'''),
    ('''bert.cls''', '''cls'''),
    ('''bert.classifier''', '''cls'''),
    ('''token_type_embeddings_visual''', '''visual_token_type_embeddings'''),
    ('''position_embeddings_visual''', '''visual_position_embeddings'''),
    ('''projection''', '''visual_projection'''),
]

lowerCamelCase__  =					[
    '''nlvr2_coco_pre_trained.th''',
    '''nlvr2_fine_tuned.th''',
    '''nlvr2_pre_trained.th''',
    '''vcr_coco_pre_train.th''',
    '''vcr_fine_tune.th''',
    '''vcr_pre_train.th''',
    '''vqa_coco_pre_trained.th''',
    '''vqa_fine_tuned.th''',
    '''vqa_pre_trained.th''',
]


def 				lowerCAmelCase__       (			a__ )      ->Optional[Any]:




  '''simple docstring'''

  _UpperCamelCase           =    torch.load(a__    ,     map_location="cpu" )
  return sd


def 				lowerCAmelCase__       (			a__    ,     a__    ,     a__=rename_keys_prefix )      ->Optional[int]:




  '''simple docstring'''

  _UpperCamelCase           =    OrderedDict()
  _UpperCamelCase           =    torch.arange(config.max_position_embeddings ).expand((1, -1) )
  # detector_d = OrderedDict()
  for key in d:
    if "detector" in key:
      # detector_d[key.replace('detector.','')] = d[key]
      continue
    _UpperCamelCase           =    key
    for name_pair in rename_keys_prefix:
      _UpperCamelCase           =    new_key.replace(name_pair[0]    ,     name_pair[1] )
    _UpperCamelCase           =    d[key]
    if key == "bert.cls.predictions.decoder.weight":
      # Old bert code didn't have `decoder.bias`, but was added separately
      _UpperCamelCase           =    new_d["cls.predictions.bias"]
  return new_d


@torch.no_grad()
def 				lowerCAmelCase__       (			a__    ,     a__ )      ->List[Any]:




  '''simple docstring'''

  assert (
      checkpoint_path.split("/" )[-1] in ACCEPTABLE_CHECKPOINTS
  ), f'The checkpoint provided must be in {ACCEPTABLE_CHECKPOINTS}.'

  # Get Config
  if "pre" in checkpoint_path:
    _UpperCamelCase           =    "pretraining"
    if "vcr" in checkpoint_path:
      _UpperCamelCase           =    {"visual_embedding_dim": 512}
    elif "vqa_advanced" in checkpoint_path:
      _UpperCamelCase           =    {"visual_embedding_dim": 2_048}
    elif "vqa" in checkpoint_path:
      _UpperCamelCase           =    {"visual_embedding_dim": 2_048}
    elif "nlvr" in checkpoint_path:
      _UpperCamelCase           =    {"visual_embedding_dim": 1_024}
    else:
      raise NotImplementedError(f'No implementation found for `{checkpoint_path}`.' )
  else:
    if "vcr" in checkpoint_path:
      _UpperCamelCase           =    {"visual_embedding_dim": 512}
      _UpperCamelCase           =    "multichoice"
    elif "vqa_advanced" in checkpoint_path:
      _UpperCamelCase           =    {"visual_embedding_dim": 2_048}
      _UpperCamelCase           =    "vqa_advanced"
    elif "vqa" in checkpoint_path:
      _UpperCamelCase           =    {"visual_embedding_dim": 2_048, "num_labels": 3_129}
      _UpperCamelCase           =    "vqa"
    elif "nlvr" in checkpoint_path:
      _UpperCamelCase           =    {
          "visual_embedding_dim": 1_024,
          "num_labels": 2,
      }
      _UpperCamelCase           =    "nlvr"

  _UpperCamelCase           =    VisualBertConfig(**a__ )

  # Load State Dict
  _UpperCamelCase           =    load_state_dict(a__ )

  _UpperCamelCase           =    get_new_dict(a__    ,     a__ )

  if model_type == "pretraining":
    _UpperCamelCase           =    VisualBertForPreTraining(a__ )
  elif model_type == "vqa":
    _UpperCamelCase           =    VisualBertForQuestionAnswering(a__ )
  elif model_type == "nlvr":
    _UpperCamelCase           =    VisualBertForVisualReasoning(a__ )
  elif model_type == "multichoice":
    _UpperCamelCase           =    VisualBertForMultipleChoice(a__ )

  model.load_state_dict(a__ )
  # Save Checkpoints
  Path(a__ ).mkdir(exist_ok=a__ )
  model.save_pretrained(a__ )


if __name__ == "__main__":
   lowerCamelCase__  =					argparse.ArgumentParser()
   # Required parameters
   parser.add_argument('''orig_checkpoint_path''', type=str, help='''A path to .th on local filesystem.''')
   parser.add_argument('''pytorch_dump_folder_path''', type=str, help='''Path to the output PyTorch model.''')
   lowerCamelCase__  =					parser.parse_args()
   convert_visual_bert_checkpoint(args.orig_checkpoint_path, args.pytorch_dump_folder_path)





import enum
import os
from hashlib import shaaaa
from typing import Optional

from .. import config
from .logging import get_logger


lowerCamelCase__  =					get_logger(__name__)




class 					_UpperCAmelCase			(     enum.Enum  ):



 '''simple docstring'''




 __A			       =					'''all_checks'''
 __A			       =					'''basic_checks'''
 __A			       =					'''no_checks'''




class 					_UpperCAmelCase			(     lowerCAmelCase  ):



 '''simple docstring'''








class 					_UpperCAmelCase			(     lowerCAmelCase  ):



 '''simple docstring'''








class 					_UpperCAmelCase			(     lowerCAmelCase  ):



 '''simple docstring'''








class 					_UpperCAmelCase			(     lowerCAmelCase  ):



 '''simple docstring'''






def 				lowerCAmelCase__       (			a__    ,     a__    ,     a__=None )      ->int:




  '''simple docstring'''

  if expected_checksums is None:
    logger.info("Unable to verify checksums." )
    return
  if len(set(a__ ) - set(a__ ) ) > 0:
    raise ExpectedMoreDownloadedFiles(str(set(a__ ) - set(a__ ) ) )
  if len(set(a__ ) - set(a__ ) ) > 0:
    raise UnexpectedDownloadedFile(str(set(a__ ) - set(a__ ) ) )
  _UpperCamelCase           =    [url for url in expected_checksums if expected_checksums[url] != recorded_checksums[url]]
  _UpperCamelCase           =    " for " + verification_name if verification_name is not None else ""
  if len(a__ ) > 0:
    raise NonMatchingChecksumError(
        f'Checksums didn\'t match{for_verification_name}:\n'
        f'{bad_urls}\n'
        "Set `verification_mode='no_checks'` to skip checksums verification and ignore this error" )
  logger.info("All the checksums matched successfully" + for_verification_name )




class 					_UpperCAmelCase			(     lowerCAmelCase  ):



 '''simple docstring'''








class 					_UpperCAmelCase			(     lowerCAmelCase  ):



 '''simple docstring'''








class 					_UpperCAmelCase			(     lowerCAmelCase  ):



 '''simple docstring'''








class 					_UpperCAmelCase			(     lowerCAmelCase  ):



 '''simple docstring'''






def 				lowerCAmelCase__       (			a__    ,     a__ )      ->Dict:




  '''simple docstring'''

  if expected_splits is None:
    logger.info("Unable to verify splits sizes." )
    return
  if len(set(a__ ) - set(a__ ) ) > 0:
    raise ExpectedMoreSplits(str(set(a__ ) - set(a__ ) ) )
  if len(set(a__ ) - set(a__ ) ) > 0:
    raise UnexpectedSplits(str(set(a__ ) - set(a__ ) ) )
  _UpperCamelCase           =    [
      {"expected": expected_splits[name], "recorded": recorded_splits[name]}
      for name in expected_splits
      if expected_splits[name].num_examples != recorded_splits[name].num_examples
  ]
  if len(a__ ) > 0:
    raise NonMatchingSplitsSizesError(str(a__ ) )
  logger.info("All the splits matched successfully." )


def 				lowerCAmelCase__       (			a__    ,     a__ = True )      ->dict:




  '''simple docstring'''

  if record_checksum:
    _UpperCamelCase           =    shaaaa()
    with open(a__    ,     "rb" ) as f:
      for chunk in iter(lambda: f.read(1 << 20 )    ,     B"" ):
        m.update(a__ )
      _UpperCamelCase           =    m.hexdigest()
  else:
    _UpperCamelCase           =    None
  return {"num_bytes": os.path.getsize(a__ ), "checksum": checksum}







def 				lowerCAmelCase__       (			a__ )      ->Any:




  '''simple docstring'''

  if dataset_size and config.IN_MEMORY_MAX_SIZE:
    return dataset_size < config.IN_MEMORY_MAX_SIZE
  else:
    return False




